@inproceedings{Lopez-Paz:2014,
 author = {Lopez-Paz, David and Sra, Suvrit and Smola, Alexander J. and Ghahramani, Zoubin and Sch\"{o}lkopf, Bernhard},
 title = {Randomized Nonlinear Component Analysis},
 booktitle = {International Conference on Machine Learning (ICML)},
 year = {2014}
}

@article{hochreiter1997long,
  title={Long short-term memory},
  author={Hochreiter, Sepp and Schmidhuber, J{\"u}rgen},
  journal={Neural computation},
  volume={9},
  number={8},
  pages={1735--1780},
  year={1997},
  publisher={MIT Press}
}

@article{kingma2014adam,
  title={Adam: A method for stochastic optimization},
  author={Kingma, Diederik P and Ba, Jimmy},
  journal={arXiv preprint arXiv:1412.6980},
  year={2014}
}

@article{clevert2015fast,
  title={Fast and accurate deep network learning by exponential linear units (elus)},
  author={Clevert, Djork-Arn{\'e} and Unterthiner, Thomas and Hochreiter, Sepp},
  journal={arXiv preprint arXiv:1511.07289},
  year={2015}
}

@inproceedings{ioffe2015batch,
  title={Batch normalization: Accelerating deep network training by reducing internal covariate shift},
  author={Ioffe, Sergey and Szegedy, Christian},
  booktitle={International conference on machine learning},
  pages={448--456},
  year={2015}
}

@article{li2017cifar10,
  title={CIFAR10-DVS: An Event-Stream Dataset for Object Classification},
  author={Li, Hongmin and Liu, Hanchao and Ji, Xiangyang and Li, Guoqi and Shi, Luping},
  journal={Frontiers in neuroscience},
  volume={11},
  pages={309},
  year={2017},
  publisher={Frontiers}
}

@inproceedings{srivastava2015unsupervised,
  title={Unsupervised learning of video representations using lstms},
  author={Srivastava, Nitish and Mansimov, Elman and Salakhudinov, Ruslan},
  booktitle={International conference on machine learning},
  pages={843--852},
  year={2015}
}

@inproceedings{andrews2016transfer,
  title={Transfer representation-learning for anomaly detection},
  author={Andrews, Jerone TA and Tanay, Thomas and Morton, Edward J and Griffin, Lewis D},
  year={2016},
  organization={ICML}
}

@inproceedings{Lopez-Paz:2014,
 author = {Lopez-Paz, David and Sra, Suvrit and Smola, Alexander J. and Ghahramani, Zoubin and Sch\"{o}lkopf, Bernhard},
 title = {Randomized Nonlinear Component Analysis},
 booktitle = {International Conference on Machine Learning (ICML)},
 year = {2014}
}

@book{hawkins1980identification,
  title={Identification of outliers},
  author={Hawkins, Douglas M},
  volume={11},
  year={1980},
  publisher={Springer}
}

@article{hochreiter1997long,
  title={Long short-term memory},
  author={Hochreiter, Sepp and Schmidhuber, J{\"u}rgen},
  journal={Neural computation},
  volume={9},
  number={8},
  pages={1735--1780},
  year={1997},
  publisher={MIT Press}
}

@article{kingma2014adam,
  title={Adam: A method for stochastic optimization},
  author={Kingma, Diederik P and Ba, Jimmy},
  journal={arXiv preprint arXiv:1412.6980},
  year={2014}
}

@article{clevert2015fast,
  title={Fast and accurate deep network learning by exponential linear units (elus)},
  author={Clevert, Djork-Arn{\'e} and Unterthiner, Thomas and Hochreiter, Sepp},
  journal={arXiv preprint arXiv:1511.07289},
  year={2015}
}

@inproceedings{ioffe2015batch,
  title={Batch normalization: Accelerating deep network training by reducing internal covariate shift},
  author={Ioffe, Sergey and Szegedy, Christian},
  booktitle={International conference on machine learning},
  pages={448--456},
  year={2015}
}

@article{li2017cifar10,
  title={CIFAR10-DVS: An Event-Stream Dataset for Object Classification},
  author={Li, Hongmin and Liu, Hanchao and Ji, Xiangyang and Li, Guoqi and Shi, Luping},
  journal={Frontiers in neuroscience},
  volume={11},
  pages={309},
  year={2017},
  publisher={Frontiers}
}

@inproceedings{srivastava2015unsupervised,
  title={Unsupervised learning of video representations using lstms},
  author={Srivastava, Nitish and Mansimov, Elman and Salakhudinov, Ruslan},
  booktitle={International conference on machine learning},
  pages={843--852},
  year={2015}
}

@inproceedings{andrews2016transfer,
  title={Transfer representation-learning for anomaly detection},
  author={Andrews, Jerone TA and Tanay, Thomas and Morton, Edward J and Griffin, Lewis D},
  year={2016},
  organization={ICML}
}
@inproceedings{stallkamp2011german,
  title={The German traffic sign recognition benchmark: a multi-class classification competition},
  author={Stallkamp, Johannes and Schlipsing, Marc and Salmen, Jan and Igel, Christian},
  booktitle={Neural Networks (IJCNN), The 2011 International Joint Conference on},
  pages={1453--1460},
  year={2011},
  organization={IEEE}
}






@article{radford2015unsupervised,
  title={Unsupervised representation learning with deep convolutional generative adversarial networks},
  author={Radford, Alec and Metz, Luke and Chintala, Soumith},
  journal={arXiv preprint arXiv:1511.06434},
  year={2015}
}

@article{brendel2017decision,
  title={Decision-based adversarial attacks: Reliable attacks against black-box machine learning models},
  author={Brendel, Wieland and Rauber, Jonas and Bethge, Matthias},
  journal={arXiv preprint arXiv:1712.04248},
  year={2017}
}

@InProceedings{pmlrv80ruff18a,
  title =    {Deep One-Class Classification},
  author =   {Ruff, Lukas and Vandermeulen, Robert and Goernitz, Nico and Deecke, Lucas and Siddiqui, Shoaib Ahmed and Binder, Alexander and M{\"u}ller, Emmanuel and Kloft, Marius},
  booktitle =    {Proceedings of the 35th International Conference on Machine Learning},
  pages =    {4393--4402},
  year =   {2018},
  editor =   {Dy, Jennifer and Krause, Andreas},
  volume =   {80},
  series =   {Proceedings of Machine Learning Research},
  address =    {Stockholmsmässan, Stockholm Sweden},
  month =    {10--15 Jul},
  publisher =    {PMLR},
  pdf =    {http://proceedings.mlr.press/v80/ruff18a/ruff18a.pdf},
  url =    {http://proceedings.mlr.press/v80/ruff18a.html},
  abstract =   {Despite the great advances made by deep learning in many machine learning problems, there is a relative dearth of deep learning approaches for anomaly detection. Those approaches which do exist involve networks trained to perform a task other than anomaly detection, namely generative models or compression, which are in turn adapted for use in anomaly detection; they are not trained on an anomaly detection based objective. In this paper we introduce a new anomaly detection method—Deep Support Vector Data Description—, which is trained on an anomaly detection based objective. The adaptation to the deep regime necessitates that our neural network and training procedure satisfy certain properties, which we demonstrate theoretically. We show the effectiveness of our method on MNIST and CIFAR-10 image benchmark datasets as well as on the detection of adversarial examples of GTSRB stop signs.}
}
@article{radford2015unsupervised,
  title={Unsupervised representation learning with deep convolutional generative adversarial networks},
  author={Radford, Alec and Metz, Luke and Chintala, Soumith},
  journal={arXiv preprint arXiv:1511.06434},
  year={2015}
}
@inproceedings{masci2011stacked,
  title={Stacked convolutional auto-encoders for hierarchical feature extraction},
  author={Masci, Jonathan and Meier, Ueli and Cire{\c{s}}an, Dan and Schmidhuber, J{\"u}rgen},
  booktitle={International Conference on Artificial Neural Networks},
  pages={52--59},
  year={2011},
  organization={Springer}
}
@article{parzen1962estimation,
  title={On estimation of a probability density function and mode},
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@article{andrews2016detecting,
  title={Detecting anomalous data using auto-encoders},
  author={Andrews, Jerone TA and Morton, Edward J and Griffin, Lewis D},
  journal={International Journal of Machine Learning and Computing},
  volume={6},
  number={1},
  pages={21},
  year={2016},
  publisher={IACSIT Press}
}

@inproceedings{chen2017outlier,
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  author={Chen, Jinghui and Sathe, Saket and Aggarwal, Charu and Turaga, Deepak},
  booktitle={Proceedings of the 2017 SIAM International Conference on Data Mining},
  pages={90--98},
  year={2017},
  organization={SIAM}
}

@article{marchi2017deep,
  title={Deep Recurrent Neural Network-Based Autoencoders for Acoustic Novelty Detection},
  author={Marchi, Erik and Vesperini, Fabio and Squartini, Stefano and Schuller, Bj{\"o}rn},
  journal={Computational intelligence and neuroscience},
  volume={2017},
  year={2017},
  publisher={Hindawi}
}

@inproceedings{chong2017abnormal,
  title={Abnormal event detection in videos using spatiotemporal autoencoder},
  author={Chong, Yong Shean and Tay, Yong Haur},
  booktitle={International Symposium on Neural Networks},
  pages={189--196},
  year={2017},
  organization={Springer}
}

@article{chatfield2014return,
  title={Return of the devil in the details: Delving deep into convolutional nets},
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  year={2014}
}


@article{neumann2005combined,
  title={Combined SVM-based feature selection and classification},
  author={Neumann, Julia and Schn{\"o}rr, Christoph and Steidl, Gabriele},
  journal={Machine learning},
  volume={61},
  number={1-3},
  pages={129--150},
  year={2005},
  publisher={Springer}
}

@article{cao2003comparison,
  title={A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine},
  author={Cao, LJ and Chua, Kok Seng and Chong, WK and Lee, HP and Gu, QM},
  journal={Neurocomputing},
  volume={55},
  number={1-2},
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  year={2003},
  publisher={Elsevier}
}


@article{russakovsky2015imagenet,
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  author={Russakovsky, Olga and Deng, Jia and Su, Hao and Krause, Jonathan and Satheesh, Sanjeev and Ma, Sean and Huang, Zhiheng and Karpathy, Andrej and Khosla, Aditya and Bernstein, Michael and others},
  journal={International Journal of Computer Vision},
  volume={115},
  number={3},
  pages={211--252},
  year={2015},
  publisher={Springer}
}

@inproceedings{srivastava2015unsupervised,
  title={Unsupervised learning of video representations using lstms},
  author={Srivastava, Nitish and Mansimov, Elman and Salakhudinov, Ruslan},
  booktitle={International conference on machine learning},
  pages={843--852},
  year={2015}
}

@article{hochreiter1997long,
  title={Long short-term memory},
  author={Hochreiter, Sepp and Schmidhuber, J{\"u}rgen},
  journal={Neural computation},
  volume={9},
  number={8},
  pages={1735--1780},
  year={1997},
  publisher={MIT Press}
}

@article{arora2016understanding,
  title={Understanding deep neural networks with rectified linear units},
  author={Arora, Raman and Basu, Amitabh and Mianjy, Poorya and Mukherjee, Anirbit},
  journal={arXiv preprint arXiv:1611.01491},
  year={2016}
}

@article{asgari2015continuous,
  title={Continuous distributed representation of biological sequences for deep proteomics and genomics},
  author={Asgari, Ehsaneddin and Mofrad, Mohammad RK},
  journal={PloS one},
  volume={10},
  number={11},
  pages={e0141287},
  year={2015},
  publisher={Public Library of Science}
}

@inproceedings{srivastava2015unsupervised,
  title={Unsupervised learning of video representations using lstms},
  author={Srivastava, Nitish and Mansimov, Elman and Salakhudinov, Ruslan},
  booktitle={International conference on machine learning},
  pages={843--852},
  year={2015}
}

@inproceedings{liu2008isolation,
  title={Isolation forest},
  author={Liu, Fei Tony and Ting, Kai Ming and Zhou, Zhi-Hua},
  booktitle={Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on},
  pages={413--422},
  year={2008},
  organization={IEEE}
}


@article{xu2015learning,
  title={Learning deep representations of appearance and motion for anomalous event detection},
  author={Xu, Dan and Ricci, Elisa and Yan, Yan and Song, Jingkuan and Sebe, Nicu},
  journal={arXiv preprint arXiv:1510.01553},
  year={2015}
}


@inproceedings{kolosnjaji2016deep,
  title={Deep learning for classification of malware system call sequences},
  author={Kolosnjaji, Bojan and Zarras, Apostolis and Webster, George and Eckert, Claudia},
  booktitle={Australasian Joint Conference on Artificial Intelligence},
  pages={137--149},
  year={2016},
  organization={Springer}
}


@article{nonlinear-PCAHybrid,
author = {Chong Su and Yue Gao and Yuxiao Xie and Yong Xue and Lijun Ge and Hongguang Li},
title = {A hybrid classifier based on nonlinear-PCA and deep belief networks with applications in dysphagia diagnosis},
journal = {Computer Assisted Surgery},
volume = {22},
number = {sup1},
pages = {135-147},
year  = {2017},
publisher = {Taylor & Francis},
doi = {10.1080/24699322.2017.1389391},
    note ={PMID: 29095063},
URL = {
        https://doi.org/10.1080/24699322.2017.1389391
},
eprint = {
        https://doi.org/10.1080/24699322.2017.1389391
}
}


@article{sohaib2017hybrid,
  title={A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis},
  author={Sohaib, Muhammad and Kim, Cheol-Hong and Kim, Jong-Myon},
  journal={Sensors},
  volume={17},
  number={12},
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  year={2017},
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}


@article{you2017hybrid,
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  author={You, Wei and Shen, Changqing and Guo, Xiaojie and Jiang, Xingxing and Shi, Juanjuan and Zhu, Zhongkui},
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  number={6},
  pages={1687814017704146},
  year={2017},
  publisher={SAGE Publications Sage UK: London, England}
}

@inproceedings{krawczyk2016hybrid,
  title={Hybrid One-Class Ensemble for High-Dimensional Data Classification},
  author={Krawczyk, Bartosz},
  booktitle={Asian Conference on Intelligent Information and Database Systems},
  pages={136--144},
  year={2016},
  organization={Springer}
}


@article{song2017hybrid,
  title={A Hybrid Semi-Supervised Anomaly Detection Model for High-Dimensional Data},
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  journal={Computational intelligence and neuroscience},
  volume={2017},
  year={2017},
  publisher={Hindawi}
}

@inproceedings{schlegl2017unsupervised,
  title={Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery},
  author={Schlegl, Thomas and Seeb{\"o}ck, Philipp and Waldstein, Sebastian M and Schmidt-Erfurth, Ursula and Langs, Georg},
  booktitle={International Conference on Information Processing in Medical Imaging},
  pages={146--157},
  year={2017},
  organization={Springer}
}

@inproceedings{jiulong2017detecting,
  title={Detecting Chinese calligraphy style consistency by deep learning and one-class SVM},
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  booktitle={Image, Vision and Computing (ICIVC), 2017 2nd International Conference on},
  pages={83--86},
  year={2017},
  organization={IEEE}
}

@article{athiwaratkun2015feature,
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  year={2015}
}
@inproceedings{dosovitskiy2014discriminative,
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  pages={766--774},
  year={2014}
}

@article{ahmed2008training,
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  pages={69--82},
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@article{pan2010survey,
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@article{cao2003comparison,
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  author={Cao, LJ and Chua, Kok Seng and Chong, WK and Lee, HP and Gu, QM},
  journal={Neurocomputing},
  volume={55},
  number={1},
  pages={321--336},
  year={2003},
  publisher={Elsevier}
}

@article{widodo2007combination,
  title={Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors},
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@article{shen2008feature,
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  volume={70},
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  publisher={Springer}
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@article{neumann2005combined,
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  journal={Machine learning},
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  pages={129--150},
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  publisher={Springer}
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@article{hinton2010practical,
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@inproceedings{andrews2016transfer,
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@inproceedings{yosinski2014transferable,
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@inproceedings{vishwanathan2003simplesvm,
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  year={2003},
  organization={AAAI press}
}



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@article{scholkopf2001estimating,
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@article{scholkopf2002support,
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@misc{chollet2015keras,
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@article{erfani2016high,
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@inproceedings{Sedhain:2015,
  author    = {Suvash Sedhain and
               Aditya Krishna Menon and
               Scott Sanner and
               Lexing Xie},
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